https://github.com/bbwang1030/half-kfn
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.
Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bethgelab/cifar10_challenge
https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/ead.py
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/gen_attack.py
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
https://github.com/bingcheng45/hnr-extension
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/boyellow/adaad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/caoquanjie/LearnToPayAttention
https://github.com/carlini/nn_robust_attacks
https://github.com/carlini/pixel-deflection
https://github.com/cdluminate/advrank-pub
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cdluminate/cdluminate
https://github.com/cfinlay/logbarrier
The LogBarrier adversarial attack: making effective use of decision boundary information
https://github.com/chamikara1986/BA-BAM
https://github.com/charchit7/QuickDraw-App
https://github.com/chawins/pal
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/chenghaomou/text-dedup
https://github.com/cihangxie/DAG
Adversarial Examples for Semantic Segmentation and Object Detection
https://github.com/cleverhans-lab/cleverhans
https://github.com/cleverhans-lab/cleverhans/blob/master/cleverhans_v3.1.0/cleverhans/attacks/elastic_net_method.py
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
https://github.com/clip-italian/clip-italian
https://github.com/coallaoh/whitenblackbox
Delving into Transferable Adversarial Examples and Black-box Attacks
https://github.com/code-implementation1/Code5/tree/main/MTCNN
Real-world adversarial attack on MTCNN face detection system
https://github.com/cs-giung/course-dl-TP
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cv-stuttgart/detectiondefenses
Local Gradients Smoothing: Defense against localized adversarial attacks
https://github.com/dacostaHugo/Adversarial_attacks
https://github.com/deqncho2/foolbox-adversarial
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
https://github.com/dongdongdong666/CPGAN
https://github.com/dsn-2024/dsn
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/dungxibo123/neural_ode
https://github.com/dwightnw/graph_based_methods_for_adversarial_attack
Graph-based methods coupled with specific distributional distances for adversarial attack detection
https://github.com/ed-zh/parden
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/edwardjhu/improved_wasserstein
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
https://github.com/ee17b031-iittp/Projected-Gradient-Descent-with-CIFAR10
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/eetkim/physatt
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
https://github.com/eldadp100/Towards-Deep-Learning-Models-Resistant-to-Adversarial-Attacks-Implementation
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/eldadp100/cnn_course_final
Towards Deep Learning Models Resistant to Adversarial Attacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.